Yazar "Sindhu, Tabassum Naz" seçeneğine göre listele

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    Comparative study of artificial neural network versus parametric method in COVID-19 data analysis
    (Elsevier, 2022) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum Naz; Lone, Showkat Ahmad; Alsubie, Abdelaziz; Jarad, Fahd
    Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was -0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.
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    Estimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling
    (Nature Portfolio, 2021) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum Naz; Al-Mdallal, Qasem M.; Abdeljawad, T.
    In current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg-Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.
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    Modeling of Darcy-Forchheimer bioconvective Powell Eyring nanofluid with artificial neural network
    (Elsevier, 2022) Colak, Andac Batur; Shafiq, Anum; Sindhu, Tabassum Naz
    Nano-engineering has recently grown to include the usages of nanoparticles in combination with base fluids to improve the thermal properties of pure fluids. Today's industry focuses primarily on thermal machine efficiency, and nanomaterials are the key to achieving this goal. The slip and Darcy-Forchheimer phenomena are studied for bioconvective implementations in a Powell-Eyring nanofluid model confined by a stretching surface via artificial neural network in current study. During the research, the activation energy, convective boundary condition and thermal radiation phenomena are considered as novel impacts. The governing expressions are formulated according to fundamental rules. Numerical simulations using a Runge-Kutta fourth order technique via shooting procedure are used to obtain the solution and then applies artificial neural network. A data set has been created for various flow scenarios, and developed an artificial neural network model to predict skin friction coefficient, local Sherwood number, local motile density of microorganisms and local Nusselt number values . The results of the study showed that the developed artificial neural network models can make predictions with very low error, not exceeding 0.53% on average.
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    Modeling of Soret and Dufour's Convective Heat Transfer in Nanofluid Flow Through a Moving Needle with Artificial Neural Network
    (Springer Heidelberg, 2023) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum Naz
    In this study, forced convective heat and mass transfer of a nanofluid using the Buongiorno model and moving radially through a thin needle has been analyzed using the Runge-Kutta fourth-order technique with shooting approach. In order to analyze the thermo-diffusion and diffusion-thermoeffects on the flow, Dufour and Soret effects have been investigated and the mass transport phenomenon has also been investigated by activation energy. Partial differential systems of the flow model have been obtained with the boundary-layer approach and modified by using the appropriate transformations to be connected to nonlinear ordinary differential systems. The Runge-Kutta technique is the most popular methodology for obtaining the numerical results to solve the differential equations. It can evaluate higher-order numerical solutions and provide answers that are as close to correct solution. Therefore, using the Runge-Kutta fourth-order strategy with a shooting strategy, a data set has been created for different flow scenarios of the interesting and comprehensive model for nanofluid (Boungiorno's model), which incorporates Brownian motion and thermophoresis. Using this data set, an artificial neural network model has been developed to predict skin friction coefficient, Sherwood number and Nusselt number values. Seventy percentage of the data used in ANN models developed with different numbers of datasets have been used for training, 15% for validation and 15% for testing. The results show that ANN models can predict skin friction coefficient, Sherwood number and Nusselt number values with error rates of - 0.33%, 0.08% and 0.03%, respectively.
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    Optimization of Bioconvective Magnetized Walter's B Nanofluid Flow towards a Cylindrical Disk with Artificial Neural Networks
    (Mdpi, 2022) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum Naz
    Nanotechnology is a fundamental component of modern technology. Researchers have concentrated their efforts in recent years on inventing various algorithms to increase heat transmission rates. Using nanoparticles in host fluids to dramatically improve the thermal properties of ordinary fluids is one way to address this problem. The article deals with the bio-convective Walter's B nanofluid with thermophoresis and Brownian diffusion through a cylindrical disk under artificial neural networks (ANNs). In addition, the thermal conductivity, radiation, and motile density of microorganisms are taken into consideration. The Buongiorno model is utilized to investigate the properties of nanofluids in motile microorganisms. By using appropriate similarity variables, a dimensionless system of a differential system is attained. The non-linear simplified system of equations has been numerically calculated via the Runge-Kutta fourth-order shooting process. The consequences of flow parameters on the velocity field, temperature distribution, species volumetric concentration, and microorganism fields are all addressed. Two distinct artificial neural network models were produced using numerical data, and their prediction performance was thoroughly examined. It is noted that according to the error histograms, the ANN model's training phase has very little error. Furthermore, mean square error values calculated for local Nusselt number, local Sherwood number, and local motile density number, parameters were obtained as 3.58x10-3, 1.24x10-3, and 3.55x10-5, respectively. Both artificial neural network models can predict with high accuracy, according to the findings of the calculated performance parameters.
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    OPTIMIZATION OF DARCY-FORCHHEIMER SQUEEZING FLOW IN NONLINEAR STRATIFIED FLUID UNDER CONVECTIVE CONDITIONS WITH ARTIFICIAL NEURAL NETWORK
    (Begell House Inc, 2022) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum Naz; Muhammad, Taseer
    In cases when high velocity occurs, non-Darcy phenomena are essential for explaining fluid motion in porous media and have wide range of applications. The present study displays the magnetohydrodynamic (MHD) squeezing flow of fluid through a non-Darcian medium towards a stretched permeable surface. The heat and mass procedures are investigated using convective conditions and nonlinear stratification. The radiation and viscous dissipation phenomena are implemented to enhance the heat transfer. The nonlinear simplified equations are evaluated using a numerical Runge-Kutta fourth-order approach via the shooting process. To see the variation in the relevant fields, graphs of essential parameters have been provided. The Sherwood number, Nusselt number, and the skin friction coefficient were calculated numerically for various parameters and three different artificial neural networks (ANNs) were developed with the obtained data. The obtained results have shown that artificial neural networks can make predictions and optimizations with high accuracy.
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    Optimization of the numerical treatment of the Darcy-Forchheimer flow of Ree-Eyring fluid with chemical reaction by using artificial neural networks
    (Wiley, 2023) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum Naz
    In this study, Darcy Forchheimer flow paradigm, which is a useful paradigm in fields such as petroleum engineering where high flow velocity effects are common, has been analyzed with artificial intelligence approach. In this context, first of all, Darcy-Forchheimer flow of Ree-Eyring fluid along a permeable stretching surface with convective boundary conditions has been examined and heat and mass transfer mechanisms have been investigated by including the effect of chemical process, heat generation/absorption, and activation energy. Cattaneo-Christov heat flux model has been used to analyze heat transfer properties. Within the scope of optimizing Darcy-Forchheimer flow of Ree-Eyring fluid; three different artificial neural network models have been developed to predict Nusselt number, Sherwood number, and skin friction coefficient values. The developed artificial neural network model has been able to predict Nusselt number, Sherwood number, and skin friction coefficient values with high accuracy. The findings obtained as a result of the study showed that artificial neural networks are an ideal tool that can be used to model Darcy-Forchheimer Ree-Eyring fluid flow towards a permeable stretch layer with activation energy and a convective boundary condition.
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    Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network
    (Wiley-V C H Verlag Gmbh, 2022) Shafiq, Anum; Colak, Andac Batur; Swarup, Chetan; Sindhu, Tabassum Naz; Lone, Showkat Ahmad
    The study of reliability analysis of mixture model is essential in confirming the quality of devices, equipment, and electronic tube flops etc. In recent years, statisticians have developed more interest in mixture model research, notably in the last decade, without taking into account the issue of modeling the metrics of reliability of mixture models using artificial neural networks. In the present study, the influence of pertinent parameters on reliability metrics is studied. The effect of components and mixing parameters for failure function, reversed hazard rate function, mean time to failure, hazard rate function, mean inactivity time, mean residual life, reliability function, Mills Ratio profiles are plotted and discussed. A multi-layer artificial neural network is developed using the numerical analysis results obtained using four different scenarios. The values extracted from the artificial neural network and the numerical findings of the reliability studies are extensively compared and examined. The deviation rates obtained for the developed artificial neural network model are obtained at values lower than 0.12%. The outcomes demonstrate that neural networks are a powerful and effective mathematical tool that can be used in the reliability analysis of mixing models.
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    Reliability investigation of exponentiated Weibull distribution using IPL through numerical and artificial neural network modeling
    (Wiley, 2022) Shafiq, Anum; Colak, Andac Batur; Sindhu, Tabassum Naz
    In current investigation, a novel implementation of intelligent numerical computing solver depending on artificial neural networks (ANN) has been provided to interpret failure function (FF), reliability function (RF), hazard rate function (HRF), Mils ratio (MR), and mean time to failure (MTTF). This study investigates a reliability model centered on the exponentiated Weibull distribution (EWD) and the inverse power law (IPL) model employing the ANN model. The nonmonotonic failure rate can be modeled via this distribution. A data set for the proposed ANN has been generated for various scenarios of (Exponentiated Weibull Inverse Power Law Distribution) EWIPLD model by variation of involved pertinent parameters via the Galerkin weighted residual method (GWRM). Levenberg-Marquard training algorithm has been used in the multi-layer perceptron (MLP) network model developed with 10 nodes in the hidden layer. The Coefficient of Determination (R) value for the ANN model has been obtained as 0.9999. The findings obtained, revealed that ANNs are an excellent technique that can be applied to predict reliability measures in conjunction with the right statistical model.
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    Reliability modeling and analysis of mixture of exponential distributions using artificial neural network
    (Wiley, 2024) Shafiq, Anum; Colak, Andac Batur; Lone, Showkat Ahmad; Sindhu, Tabassum Naz; Muhammad, Taseer
    In recent years, statisticians have become more and more interested in the study of mixture models, especially in the last decade, without adequately considering the difficulty of modeling the reliability measures of mixture models using artificial neural networks. In this study, in which artificial neural networks and mixed model reliability criteria are analyzed, various reliability parameters are calculated considering different scenarios. In order to estimate the obtained numerical reliability parameters, a multilayer artificial neural network model has been developed. Seven different reliability parameter values have been obtained from the artificial neural network model designed with four input parameters. The prediction values obtained from the artificial neural network model developed with five neurons in the hidden layer have been compared with numerical data, and the performance of the model has been analyzed comprehensively. The mean squared error (MSE) value for the network model has been calculated as 1.98E-08 and the R value as 0.99991. The results clearly revealed that the artificial neural network model developed using data from the appropriate statistical model is an excellent tool that can be used to estimate reliability measures.